Learning Subgroups with Maximum Treatment Effects Without Causal Heuristics

Lincen Yang; Zhong Li; Matthijs van Leeuwen; Saber Salehkaleybar

AAAI 2026technical0 citations

Learning Subgroups with Maximum Treatment Effects Without Causal Heuristics

Lincen Yang, Zhong Li, Matthijs van Leeuwen, Saber Salehkaleybar

Abstract

Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While most prior work is formulated in the potential‑outcome framework, the corresponding structural causal model (SCM) for this task has been largely overlooked. In practice, two approaches dominate. The first estimates pointwise conditional treatment effects and then fits a tree on those estimates, effectively turning subgroup estimation into the harder problem of accurate pointwise estimation. The second constructs decision trees or rule sets with ad‑hoc

BibTeX

@inproceedings{aaai2026_learningsubgroup,
  title = {Learning Subgroups with Maximum Treatment Effects Without Causal Heuristics},
  author = {Lincen Yang and Zhong Li and Matthijs van Leeuwen and Saber Salehkaleybar},
  booktitle = {AAAI 2026},
  year = {2026}
}